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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/1CA9A46C-5FE8-486F-BCD6-469104FE4C80 | - |
| Identifier | https://doi.org/10.3390/app10020700 | - |
| Identifier | https://www.mdpi.com/2076-3417/10/2/700/htm | - |
| Language | en | - |
| Extent | 19 pages | en |
| Extent | 7,28 megabytes | en |
| Title | Global MPPT based on machine-learning for PV arrays operating under partial shading conditions | en |
| Creator | Kalogerakis Christos | en |
| Creator | Καλογερακης Χρηστος | el |
| Creator | Koutroulis Eftychios | en |
| Creator | Κουτρουλης Ευτυχιος | el |
| Creator | Lagoudakis Michail | en |
| Creator | Λαγουδακης Μιχαηλ | el |
| Publisher | MDPI | en |
| Description | This article belongs to the special issue Advancing grid-connected renewable generation systems 2019 | en |
| Content Summary | A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-09-27 | - |
| Date of Publication | 2020 | - |
| Subject | Machine learning | en |
| Subject | Maximum power point tracking (MPPT) | en |
| Subject | Particle swarm optimization (PSO) | en |
| Subject | Photovoltaic systems | en |
| Subject | Reinforcement learning | en |
| Subject | Q-learning | en |
| Bibliographic Citation | C. Kalogerakis, E. Koutroulis, and M. G. Lagoudakis, “Global MPPT based on machine-learning for PV arrays operating under partial shading conditions,” Appl. Sci., vol. 10, no. 2, Jan. 2020. doi: 10.3390/app10020700 | en |
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